基于匹配实测表面肌电信号的模型参数辨识

目的 通过对仿真与真实表面肌电信号(sEMG)的波形匹配以及肌疲劳现象的分析,研究sEMG信号的模型参数辨识问题. 方法 在运动单位仿真的基础上,引入神经激励对运动单位的募集和发放控制特性,建立了一个较为完善的sEMG信号生理学模型.利用调整模型相关生理参数使仿真与真实sEMG信号的运动单位动作电位(MUAP)波形相匹配的方法,实现对模型参数进行估计,通过调节肌纤维传导速度(MFCV)使仿真与真实sEMG信号的平均频率(MNF)及中值频率(MDF)拟合直线趋势相似的方法,研究肌肉的疲劳现象及其机理. 结果 适当调节sEMG信号模型参数可使仿真信号波形逼近真实sEMG信号波形,各个肌纤维的MFCV在模拟恒力持续收缩过程中减小时,仿真信号的MNF和MDF拟合直线呈下降趋势. 结论 采用模型方法能够实现仿真与真实sEMG信号波形的良好匹配,并能够有效地表达肌肉的疲劳过程,可应用于肌电信号相关领域的研究.

Identification of Model Parameters Basing on Matched Processing between Simulated and Recorded sEMG Signals

Objective To identify the model parameters of surface Electromyography(sEMG) by comparison between simulated and recorded signals.Methods A physiological model of sEMG signal was established basing on several logical hypothetical conditions,such as motor unit action potentials(MUAP),motor unit recruitment and firing behavior caused by excitation,architecture of volume conductor and other simulated factors.According to the matched shapes between the simulated and recorded sEMG signals,a group of model parameters was obtained;according to the similar power spectrum variations of real sEMG signals,decreased muscle fiber conduction velocity(MFCV) was applied to simulate the sEMG signals of the fatigued muscle.Results The experimental results showed that the simulated superimposed MUAP shapes could be matched with the recorded MUAPs satisfactorily by adjusting some proper physiological parameters of the model.When the MFCV of each fiber was assumed to decrease,the mean and median frequency(MNF,MDF) of the simulated sEMG signals declined,and this phenomenon was very similar to that of the recorded sEMG signals and could be used to interpret the muscle fatigue process.Conclusion This model provides an effective approach to simulate real sEMG signals,and the simulated signals can also be used to help the analysis of recorded sEMG signals.